Mask-RCNN detection of COVID-19 pneumonia symptoms by employing Stacked Autoencoders in deep unsupervised learning on Low-Dose High Resolution CT

Mask-RCNN detection of COVID-19 pneumonia symptoms by employing Stacked Autoencoders in deep unsupervised learning on Low-Dose High Resolution CT

Citation Author(s):
Zhi-Hao
Chen
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Submitted by:
Zhihao Chen
Last updated:
Fri, 05/01/2020 - 09:23
DOI:
10.21227/4kcm-m312
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This paper applies AI (artificial intelligence) technology to analyze low-dose HRCT (High-resolution chest radiography) data in an attempt to detect COVID-19 pneumonia symptoms. A new model structure is proposed with segmentation of anatomical structures on DNNs-based (deep learning neural network) methods, relying on an abundance of labeled data for proper training. The model improves the existing techniques used for low-dose HRCT image inspection through an application of Stacked Autoencoders (SAEs) structures using the segmentation function for the area object detection model on Mask-RCNN. As a result, the proposed approach can quickly analyze X-ray images in detecting abnormalities in patients with lab-confirmed coronavirus even before clinical symptoms appear. In addition to detecting early abnormalities, area object detection model reveals a finding not seen in the latest cases of COVID-19. Most noteworthy, the study has shown that all COVID-19 patients exhibit an associated bilateral pleural effusion. The features are augmented to the model for the improvement of detection quality improvement and the shorten of the examination period.

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This tool model propose a Mask-RCNN detection of COVID-19 pneumonia symptoms by employing Stacked Autoencoders in deep unsupervised learning on Low-Dose High Resolution CT architecture. Based on autoencoder of Mask-RCNN for area mark feature maps objection detection for the identification of COVID-19 pneumonia have very serious pathological and always accompanied by various of symptoms. We collect a lot of lung x-ray images were be integrated into DICM style dataset prepare for experiment on computer on vision algorithms, and deep learning architecture based on autoencoder of Mask- RCNN algorithms are the main technological breakthrough.

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[1] Zhi-Hao Chen, "Mask-RCNN detection of COVID-19 pneumonia symptoms by employing Stacked Autoencoders in deep unsupervised learning on Low-Dose High Resolution CT", IEEE Dataport, 2020. [Online]. Available: http://dx.doi.org/10.21227/4kcm-m312. Accessed: May. 30, 2020.
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doi = {10.21227/4kcm-m312},
url = {http://dx.doi.org/10.21227/4kcm-m312},
author = {Zhi-Hao Chen },
publisher = {IEEE Dataport},
title = {Mask-RCNN detection of COVID-19 pneumonia symptoms by employing Stacked Autoencoders in deep unsupervised learning on Low-Dose High Resolution CT},
year = {2020} }
TY - DATA
T1 - Mask-RCNN detection of COVID-19 pneumonia symptoms by employing Stacked Autoencoders in deep unsupervised learning on Low-Dose High Resolution CT
AU - Zhi-Hao Chen
PY - 2020
PB - IEEE Dataport
UR - 10.21227/4kcm-m312
ER -
Zhi-Hao Chen. (2020). Mask-RCNN detection of COVID-19 pneumonia symptoms by employing Stacked Autoencoders in deep unsupervised learning on Low-Dose High Resolution CT. IEEE Dataport. http://dx.doi.org/10.21227/4kcm-m312
Zhi-Hao Chen, 2020. Mask-RCNN detection of COVID-19 pneumonia symptoms by employing Stacked Autoencoders in deep unsupervised learning on Low-Dose High Resolution CT. Available at: http://dx.doi.org/10.21227/4kcm-m312.
Zhi-Hao Chen. (2020). "Mask-RCNN detection of COVID-19 pneumonia symptoms by employing Stacked Autoencoders in deep unsupervised learning on Low-Dose High Resolution CT." Web.
1. Zhi-Hao Chen. Mask-RCNN detection of COVID-19 pneumonia symptoms by employing Stacked Autoencoders in deep unsupervised learning on Low-Dose High Resolution CT [Internet]. IEEE Dataport; 2020. Available from : http://dx.doi.org/10.21227/4kcm-m312
Zhi-Hao Chen. "Mask-RCNN detection of COVID-19 pneumonia symptoms by employing Stacked Autoencoders in deep unsupervised learning on Low-Dose High Resolution CT." doi: 10.21227/4kcm-m312